A Machining State-Based Approach to Tool Remaining Useful Life Adaptive Prediction

Abstract

The traditional predictive model for remaining useful life predictions cannot achieve adaptiveness, which is one of the main problems of said predictions. This paper proposes a LightGBM-based Remaining useful life (RUL) prediction method which considers the process and machining state. Firstly, a multi-information fusion strategy that can effectively reduce the model error and improve the generalization ability of the model is proposed. Secondly, a preprocessing method for improving the time precision and small-time granularity of feature extraction while avoiding dimensional explosion is proposed. Thirdly, an importance coefficient and a custom loss function related to the process and machining state are proposed. Finally, using the processing data of actual tool life cycle, through five evaluation indexes and 25 sets of contrast experiments, the superiority and effectiveness of the proposed method are verified.

Evaluation of Thermal Error Compensation Strategies Regarding Their Influence on Accuracy and Energy Efficiency of Machine Tools

“…Nevertheless, optimizing cutting parameters to maximize productivity is sensible and there are numerous methods of dealing with the aforementioned issues, e.g. predicting the thermal errors of tool and workpiece, predicting the static deflection and dynamic operating limits of the machine tool and even estimating the remaining useful tool life [9][10][11][12]. Energy optimization via improved cutting parameters was investigated and achieved, e.g., by Li et al [13] and Han et al [14].…”